If your (probabilistic) model governing the data creation is well-specified, then the method of Maximum Likelihood Estimation is literally finding those parameters that optimize the likelihood function given the observed data.

Under standard assumptions, the likelihood function is the product of the probability model n times (number of data observations). This function will have specific form in the parameters.

Suggest the following to get you started:

https://en.wikipedia.org/wiki/Maximum_likelihood

https://books.google.at/books?id=9CaoAqaRcVwC&pg=PA281&lpg=PA281&dq=maximum+likelihood+in+risk+analysis&source=bl&ots=tPd1hDtrR4&sig=bsTgUrxFvwm6GAInFph_lrQymjY&hl=en&sa=X&ved=0ahUKEwin5Zzt0pzLAhUrkXIKHayFDlMQ6AEIMTAE#v=onepage&q=maximum%20likelihood%20in%20risk%20analysis&f=false 

could you please explain your question more clear including ( the data type you are working on and the mathematical model)?

Dear Maryam,

Thank you that you're ready for help,

see the following articles:

http://jap.physiology.org/content/57/3/815.full-text.pdf+html

http://www.ncbi.nlm.nih.gov/pubmed/1592748